Feature extraction using mfcc
TLDR
Experimental results represents that proposed application of using MFCC for gesture recognition have very good accuracy and hence can be used for recognition of sign language or for other household application with the combination for other techniques such as Gabor filter, DWT to increase the accuracy rate and to make it more efficient.Abstract:
Mel Frequency Ceptral Coefficient is a very common and efficient technique for signal processing. This paper presents a new purpose of working with MFCC by using it for Hand gesture recognition. The objective of using MFCC for hand gesture recognition is to explore the utility of the MFCC for image processing. Till now it has been used in speech recognition, for speaker identification. The present system is based on converting the hand gesture into one dimensional (1-D) signal and then extracting first 13 MFCCs from the converted 1-D signal. Classification is performed by using Support Vector Machine. Experimental results represents that proposed application of using MFCC for gesture recognition have very good accuracy and hence can be used for recognition of sign language or for other household application with the combination for other techniques such as Gabor filter, DWT to increase the accuracy rate and to make it more efficient.read more
Citations
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Parkinson’s Disease Diagnosis in Cepstral Domain Using MFCC and Dimensionality Reduction with SVM Classifier
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TL;DR: In this article, the authors proposed a method for detecting Parkinson's disease by extracting cepstral features from the voice signals collected from people with PD and healthy subjects, using dimensionality reduction through linear discriminant analysis and classification through support vector machine.
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An efficient gesture based humanoid learning using wavelet descriptor and MFCC techniques
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References
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Posted Content
Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques
TL;DR: This paper presents the viability of MFCC to extract features and DTW to compare the test patterns and explains why the alignment is important to produce the better performance.
Speaker identification using mel frequency cepstral coefficients
Rashidul Hasan,Saifur Rahman +1 more
TL;DR: This paper presents a security system based on speaker identification based onMel frequency Cepstral Coefficients{MFCCs} have been used for feature extraction and vector quantization technique is used to minimize the amount of data to be handled.
Journal ArticleDOI
Hand Gesture Recognition Using Haar-Like Features and a Stochastic Context-Free Grammar
TL;DR: A new approach to solve the problem of real-time vision-based hand gesture recognition with the combination of statistical and syntactic analyses based on a stochastic context-free grammar is proposed.
Proceedings ArticleDOI
Hand gesture recognition using neural networks
G.R.S. Murthy,R. S. Jadon +1 more
TL;DR: A system which can identify specific hand gestures and use them to convey information and could achieve up to 89% correct results on a typical test set is designed.
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Gabor filter-based hand-pose angle estimation for hand gesture recognition under varying illumination
TL;DR: A novel approach for hand gesture recognition based on Gabor filters and support vector machine (SVM) classifiers for environments with varying illumination is presented and is robust against varying illumination and insensitive to hand-pose variations.
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